Neural activity tracking using spatial compressive particle filtering

We investigate and demonstrate the sparsity of electroencephalography (EEG) signals in the spatial domain by incorporating grid spacing in the area of the head enclosing the brain volume. We exploit this spatial sparsity and propose a new approach for tracking neural activity that is based on compressive particle filtering. Our approach results in reducing the number of EEG channels required to be stored and processed for neural tracking using particle filtering. Simulations using both synthetic and real EEG signals illustrate that the proposed algorithm has tracking performance comparable to existing methods while using only a reduced set of EEG channels.